Prostate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer:results from a multicenter study in China

• 作者: Brian T Helfand Da Huang Dan-Feng Xu Ding-Wei Ye Fang Liu Jian-Feng Xu Jun Qi Qiang Ding Rong Na Siqun L Zheng Yi-Shuo Wu Ying-Hao Sun
• 来源期刊: 亚洲男性学杂志（英文版）
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: ORIGINAL ARTICLE
• 页码范围: 539-543
• 页数: 5页
• 语种: 英文
• DOI: 10.4103/aja.aja_136_19

摘要

To evaluate whether prostate volume (PV) would provide additional predictive utility to the prostate health index (phi) for predicting prostate cancer (PCa) or clinically significant prostate cancer,we designed a prospective,observational multicenter study in two prostate biopsy cohorts.Cohort 1 included 595 patients from three medical centers from 2012 to 2013,and Cohort 2 included 1025 patients from four medical centers from 2013 to 2014.Area under the receiver operating characteristic curves (AUC) and logistic regression models were used to evaluate the predictive performance of PV-based derivatives and models.Linear regression analysis showed that both total prostate-specific antigen (tPSA) and free PSA (fPSA) were significantly correlated with PV (all P ＜ 0.05).[-2]proPSA (p2PSA) was significantly correlated with PV in Cohort 2 (P ＜ 0.001) but not in Cohort 1 (P =0.309),while no significant association was observed between phi and PV.When combining phi with PV,phi density (PHID) and another phi derivative (PHIV,calculated as phi/PV0.5) did not outperform phi for predicting PCa or clinically significant PCa in either Cohort 1 or Cohort 2.Logistic regression analysis also showed that phi and PV were independent predictors for both PCa and clinically significant PCa (all P ＜ 0.05);however,PV did not provide additional predictive value to phi when combining these derivatives in a regression model (all models vs phi were not statistically significant,all P＞ 0.05).In conclusion,PV-based derivatives (both PHIV and PHID) and models incorporating PV did not improve the predictive abilities of phi for either PCa or clinically significant PCa.